An advanced course covering anatomical, physiological, behavioral, and computational studies of the central nervous system relevant to speech and hearing. Students learn primarily by discussions of scientific papers on topics of current interest. Recent topics include cell types and neural circuits in the auditory brainstem, organization and processing in the auditory cortex, auditory reflexes and descending systems, functional imaging of the human auditory system, quantitative methods for relating neural responses to behavior, speech motor control, cortical representation of language, and auditory learning in songbirds.

A two-day introduction to modern evidence-based teaching practices, built and maintained by the Carpentry community.

The book Current and Emerging Trends in the Management of International Disaster explores contemporary practices and strategies in managing global disasters. It offers an overview of current trends, challenges, and innovations in the field, emphasizing the importance of international cooperation and effective management strategies.

Software Carpentry lesson that teaches how to use databases and SQL In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.

This open textbook was the result of a remix of pre-existing open materials collected and reviewed by Molly Zhou and David Brown. Learning theories covered include the theories of Piaget, Bandura, Vygotsky, Kohlberg, Dewey, Bronfenbrenner, Eriksen, Gardner, Bloom, and Maslow.

Short Description:
This is a collection of resources to complement ENGL128 Essentials of Communication, an introduction to the fundamentals of effective speaking and writing, exploring a variety of contexts in which language is used.

Word Count: 42931

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

Short Description:
This book is the result of a co-design project in a class in the Masters of Education program at the University of Calgary. The course, and the resulting book, focus primarily on the safe and ethical use of technology in digital learning environments. The course was organized according to four topics based on Farrow’s (2016) Framework for the Ethics of Open Education.

Long Description:
Click on Volume 1 to read the first book in this series.

This book is the result of a co-design project in a class in the Masters of Education program at the University of Calgary. The course, and the resulting book, focus primarily on the safe and ethical use of technology in digital learning environments, and is the second volume in the series. The course was organized according to four topics based on Farrow’s (2016) Framework for the Ethics of Open Education. Students were asked to review, analyze, and synthesize each topic from three meta-ethical theoretical positions: deontological, consequentialist, and virtue ethical (Farrow, 2016). The chapters in this open educational resource (OER) were co-designed using a participatory pedagogy with the intention to share and mobilize knowledge with a broader audience. The first section, comprised of four chapters, focuses on topics relating to well-being in technology-enabled learning environments, including the use of web cameras, eproctoring software, video games, and access to broadband connectivity. The second section focuses on privacy and autonomy of learners and citizens in a variety of contexts from schools to clinical settings. In each of the seven chapters, the authors discuss the connection to the value of technology in education, and practical possibilities of learning technologies for inclusive, participatory, democratic, and pluralistic educational paradigms. The book concludes with reflections from the course instructor gained over two iterations of teaching the course.

Word Count: 40312

ISBN: 978-0-88953-472-8

(Note: This resource's metadata has been created automatically by reformatting and/or combining the information that the author initially provided as part of a bulk import process.)

This document is intended to provide practical guidelines for researchers to follow when examining their data for missingness and making decisions about how to handle that missingness. We primarily offer recommendations for multiple imputation, but also indicate where the same decisional guidelines are appropriate for other types of missing data procedures such as full information maximum likelihood (FIML). Streamlining procedures to address missing data and increasing the transparency of those procedures through consensus on reporting standards is inexorably linked to the goals of open scholarship (i.e., the endeavour to improve openness, integrity, social justice, diversity, equity, inclusivity and accessibility in all areas of scholarly activities, and by extension, academic fields beyond the sciences and academic activities; Pownall et al., 2021). Successfully implementing transparent and accessible guidelines for addressing missing data is also important for Diversity, Equity, Inclusion, and Accessibility (DEIA) improvement efforts (Randall et al., 2021). Structural barriers to participation in research can lead to participants from minoritized groups disproportionately dropping out of longitudinal, developmental studies or not completing measures (Randall et al., 2021). This selection effect can bias model estimates and confidence intervals, leading to unsubstantiated claims about equitable outcomes. In addition to often creating artificially small estimates of inequalities between groups, listwise deletion also limits statistical power for minoritized groups who are already underrepresented in many datasets.

This lesson is part of Software Carpentry workshops and teach an introduction to plotting and programming using python. This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example, and is designed to be used in both Data Carpentry and Software Carpentry workshops. This lesson references JupyterLab, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.

In the fall of 2021, students in Pseudoscience courses started creating this open educational resource (OER), which has been built upon by subsequent classes. Our intention is to create a free textbook for this course that might also be used by students of critical thinking elsewhere and of all ages, whether in a classroom or not. Our growing, interactive textbook employs the Paul-Elder Model and other critical-thinking resources, and is freely available to all, learners and educators alike.

The topic of pseudoscience offers a rewarding way for students to learn the value of thinking critically, even as they get to argue things, like Flat Earth Theory and astrology, that may seem trivial at first. At a time when truth is understood as largely subjective, we have, not surprisingly, seen a resurgence in the popularity of pseudosciences and conspiracy theories, which many consider to hold significant truth value, just as valid as physical evidence. It is our aim here to demonstrate the reasoned analysis process — weighing truth, belief, opinion, and fact — so that others may be able to replicate this process and reason through their own questions about vaccines, extra-terrestrials, genetic modification, or the first people to arrive in the Americas.

Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.

For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).

This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.

Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.

For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).

This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.

Software Carpentry lesson on how to use the shell to navigate the filesystem and write simple loops and scripts. The Unix shell has been around longer than most of its users have been alive. It has survived so long because it’s a power tool that allows people to do complex things with just a few keystrokes. More importantly, it helps them combine existing programs in new ways and automate repetitive tasks so they aren’t typing the same things over and over again. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources (including “high-performance computing” supercomputers). These lessons will start you on a path towards using these resources effectively.

Who Built America? includes a free online textbook, primary document repository, and teaching resource created by the American Social History Project/Center for Media and Learning at the Graduate Center of the City University of New York. The textbook and supplemental resources survey the nation’s past from an important but often neglected perspective—the transformations wrought by the changing nature and forms of work, and the role that working people played in the making of modern America.

Who Built America? offers a thirty-chapter textbook accompanied by drawings, paintings, prints, cartoons, photographs, objects, and other visual media, including links to ASHP/CML’s ten documentary videos and teacher guides that supplement the book’s themes and narrative and offer perspectives on the past that were often not articulated in the written record. Each chapter includes first-person “Voices” from the past—excerpts from letters, diaries, autobiographies, poems, songs, journalism, fiction, official testimony, oral histories, and other historical documents—along with a timeline and suggestions for further reading.

This online edition features supplemental materials designed to help readers understand the practice of history. The more than forty A Closer Look essays, offer readers an in-depth investigation of a significant historical event, cultural phenomenon, or trend that is otherwise only touched upon in a chapter. The seven Historians Disagree essays provide readers with historiographic perspectives on how scholars’ approaches to key topics have changed over time, illuminating how history is an ever-evolving field of study.

The OER also includes the History Matters Repository, featuring more than 2,000 primary source resources from the History Matters: The U.S. Survey Course on the Web site. The items in this fully searchable repository contain contextual headnotes and links to related documents.